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The Biochemical and Physiological Responses of Consistent Exercise

The exercise involves running, swimming, cycling, skiing, and other aerobic activities. The biochemical and physiological needs of consistent exercise induce muscle and system-based responses. Regular exercise increases muscle tensile strength and stiffness. According to a study by Tung et al. (2019), the rabbits found out that in 40 weeks of running on the machines, roughly 10% of the tibialis and the Achilles posterior tendons had increased stiffness and 5% increment on the same tendons. A similar study by Khan et al. (2017) found that mice had increased tensile strength and stiffness after rats were subjected to swimming and training exercises.

Research shows that consistent exercise results in varying biochemical composition in the muscle tendons. During exercise, studies have shown that non-collagenous materials get diminished in the exterior tendons in exterior tendons, hence increasing the concentration of collagen proteins, changing the specific tendons' chemical properties.

Tung et al. (2019) suggest that consistent exercise prevents lipid abnormalities and conditions like diabetes mellitus, obesity, and hypertension, making it beneficial to sedentary individuals. Like humans, animals show different responses to exercises due to their genetic variations. In Tung's study, it was revealed that mice who participated in High exercise capacity outperformed mice who participated in low excise capacities in physical activities and showed a higher glucose tolerance. This shows that strenuous activities induce peripheral fatigue and lower the level of biomarkers in mice participating in higher exercise capacity. Besides, the gut of mice exposed to higher exercise capacity contained more Butyrucucoccus than mice exposed to low exercise capacity.

Therefore, it is justifiable that physical exercise influences the structure, mechanical and chemical composition of the muscles. Besides, regular physical activities can help reduce morbidity and all-cause mortality, including preventing diabetes, obesity, dyslipidemia, and hypertension (Tung et al., 2019). The major impact resulting from consistent exercise is improved metabolic, mechanical, metabolic, contractile, and neuromuscular function in the muscles; rebalancing the electrolytes; reducing glycogen stores, and increasing mitochondrion biogenic in the muscles (Russel et al. 2013).  

A tracker fitness refers to the wrist-worn device capable of detecting combinations of walking steps, heart rate, running distance, sleep patterns, and swimming laps. These devices can interact via Bluetooth with mobile app devices that configure and download the individual’s activity data. Even though fitness tracking devices are wearable, not all wearable devices have fitness tracking functionalities.

Research shows that fitness technologies increase individuals' physical activities level (Etkin 2016). This is made possible by encouraging behavior change, affecting the individual's wellness programs. Many studies agree that fitness trackers increase the level of moderate-to-vigorous activities. Wearable tracking devices have shown positive outcomes on increasing the average steps made on health

The major purpose of this research paper was to evaluate the use of fitness trackers and determine if their use would positively impact the student's physical activity behaviors, wellness knowledge, and perception in general. This study answers the following research question “Does the use of fitness trackers increase physical activities in University Students?

This study was performed in 2 phases.

Effect of Consistent Exercise on Muscle Tensile Strength and Stiffness

Participants in this phase were male students studying at the University of Reading. A total of 50 participants were recruited in this phase. The age of the participants ranged between 18-21 years. The inclusion criteria were that every participant had to be reasonably fit and healthy with no pre-existing health conditions that may prevent the participant from exercising.

In phase 2, 40 female students studying at the University of Reading were recruited for this study. The age of the female student selected ranges between 18-21 years old. Only participants who were considered to be healthy and with no pre-existing conditions were included in the study.

In the first phase, after all the pre-test were done, participants were divided into two students (N=25) each. The first groups (N=25) were given a fitness tracker and instructed to track their daily physical activities. The other students were also given the tracker, but they could not see their physical data. Both groups wore their fitness trackers for 6 weeks.

During this time of data collection, each participant was asked to change their fitness tracker after ensuring that all the data was recorded. The tracker was set to record heart rate at 160 bpm every 10 minutes. The data collected in the phase were tabulated as shown in appendix 1.

In the second phase, 40 students recruited were divided into two, with the first group (N=20) were given fitness trackers and could see their physical data while the second group was given fitness, but they could not see their data. The number of sessions was recorded and summarized in tabular forms in both phases.

One way analysis of variance was performed using Minitab to measure the mean differences between genders with fitness trackers over the 6 weeks of daily exercise. Paired Student’ T-test was preferred for measuring the changes in the physical activity levels among the participants. Also, the correlation analysis was used to determine if there existed a relationship between the phases.

It was hypothesized that there was no significant difference in the number of exercise sessions taken by participants who saw the tracker data compared to participants who could not see their fitness tracking data.

 The alternative hypothesis: There is a significant difference indifference in the number of exercise sessions taken by participants who were seeing the tracker data compared to participants who could not see their fitness tracking data

Sample

N

Mean

StDev

SE Mean

With Tracker

25

15.040

2.622

0.524

No Tracker

25

5.800

2.483

0.497

Table 1: Descriptive Statistics for male participants

Results in Table 1 show that the participants with tackers or who could see their tracking data had a higher mean of excursive sessions (M=15.04, SD= 2.62). In contrast, participants with no trackers had the lowest number of exercise sessions (M= 5.80, SD=2.48).

The null hypothesis was that "the mean difference between the male participants with trackers and with no trackers is zero ."The estimate for the paired difference in means of participants with trackers and those with no fitness trackers is (M=9.24, SD=3.59). There is a 95% confidence that the paired difference means for phase 1 falls between 7.76 and 10.72.

Fitness Trackers and Their Impact on Physical Activity Behaviors and Wellness Knowledge

The paired t-test shows a significant mean difference in the number of exercise sessions taken by participants who were seeing the tracker data compared to participants who could not see their fitness tracking data ( The null hypothesis is rejected in favor of the alternative hypothesis.

  graphs also indicate a significant difference between the means

The graphs also indicate a significant difference between the means of participants with trackers and those with no trackers.

Sample

N

Mean

StDev

SE Mean

With Tracker

20

11.600

2.683

0.600

No Tracker

20

5.050

2.724

0.609

 Table 2: Descriptive statistics for female participants

The results show that females with fitness trackers have more exercise sessions (M= 11.6, SD=2.68) than females with no fitting trackers (M=5.05, Sd=2.72). The null hypothesis was that “the mean difference between female participants with trackers and with no trackers is zero ."The estimate for the paired difference in means of participants with trackers and those with no fitness trackers is (M=6.55, SD=3.65). There is a 95% confidence that the paired difference means for phase 2 falls between 4.84 and 8.26.

The paired t-test shows a significant mean difference in the number of exercise sessions taken by female participants who were seeing the tracker data compared to female participants who could not see their fitness tracking data ( The null hypothesis is rejected in favor of the alternative hypothesis.

 indicate significant differences between the means of participants

The phase 2 box plots also indicate significant differences between the means of participants with trackers and those with no fitting trackers.

The one-way ANOVA analysis perfumed revealed that p<0.00, indicating a significant difference in means between female and male numbers of excise sessions taken within the six-week period.

The use of fitness trackers promotes physical activities to university students. From the results, it can be sent that students who could see their data had a higher average on the number of exercise sessions in both cases. Also, the ANOVA analysis revealed a significant difference between the number of exercise sessions taken between female and male participants. Male students had a mean of (M=15.04, SD=2.62) higher than the females’ average number of sessions (M=11.60, SD=2.68). These results course to Jin et al. (2020), which found out that physical tracking has a positive influence on the student’s motivation to be physically active (Butryn et al. 2016). Fitness trackers increase individuals' anticipated motivation when presented with goal-physical activities. According to Attig & Franke (2019), motivation for physical activities reduces when fitness trackers are not available or not in use by individuals. Many studies agree that fitness trackers have positive impacts on physical activities. However, some studies suggest that having no definite goals in mind when using fitness trackers may result in little motivation for the individuals’ physical activities (Casey et al., 2014).

This study confirms that using physical fitness trackers can increase the user's level of activities from moderate to more vigorous ones (Butryn et al., 2016). While on the individual's well-being, studies show that physical fitness tracking positively impacts the perception of an individual’s physical health. Fritz et al. (2014) suggested that physical fitness is one of the greater ways of increasing weight loss, reducing blood pressure, and thereby impacting users' subjecting happiness and satisfaction.

Despite the positive effect fitness trackers have on the students, the individual students must have a pre-exciting motivation to be more active. The results showed that students who could see their physical data were more active than students who could not access their physical activities data. Interestingly, in both cases, students with trackers have a higher mean number of exercises seen than those with no trackers. However, according to Jarrahi et al. (2018), positive impacts of fitness trackers among students can be sometimes reduced in situations, for instance, when students are too occupied with exercising, poor self-management skills, finding exercise boring, lack of support or skills, and even fearing injury that may occur during physical exercise.

It is, therefore, necessary for the individuals using the fitness tracking devices to have a stronger positive will. Ideally, when the fitness tracker is accompanied by features like social completion and other network services, individuals' pre-existing motivations are boosted (Zhu et al., 2017). Though like in the case of students' physical performance, research shows that fitness trackers increase users' self-awareness, such as activity level, the value of the activity, and task progress (Preusse et al., 2017). Casey et al. (2014) claim that fitness trackers increase task motivation by supporting individuals’ self-efficacy or basic psychological needs. There is no doubt that fitness trackers enhance the pleasure of physical activities among students, a reason why a student with trackers tends to have a higher exercise session than students with no fitness trackers in both genders.

Conclusion

An increase in physical activities among university students should continue to be the main focus, as overweight, obesity, and other lifestyle diseases continue to rise among university students. Therefore, it is critical for the University of reading to research into motivational factors that may eventually increase the probability of Reading's Students being willing to engage in various physical activities using the fitness trackers and other related digital devices.

The data used for determining the physical activities were insufficient since some features of the students were left out. Features such as body weight, heart rate, among others, were left out. This made it difficult to perform an extensive comparison analysis among the groups.

References

  1. Attig, , and T.Franke. 2019. “I Track. Therefore I Walk – Exploring the Motivational Costs of Wearing Activity Trackers in Actual Users.” International Journal of Human-Computer Studies 127: 211–224. doi:10.1016/j.ijhcs.2018.04.007.
  2. Butryn,  L., D.Arigo, G. A. Raggio, M. Colasanti, and E. M. Forman. 2016. “Enhancing Physical Activity Promotion in Midlife Women with Technology-Based Self-Monitoring and Social Connectivity: A Pilot Study.” Journal of Health Psychology 21 (8): 1548–1555. doi:10.1177/1359105314558895. 
  3. Casey, , P. S.Hayes, F. Glynn, G. ÓLaighin, D. Heaney, A. W. Murphy, and L. G. Glynn. 2014. “Patients’ Experiences of Using a Smartphone Application to Increase Physical Activity: the SMART MOVE Qualitative Study in Primary Care.” British Journal of General Practice 64 (625): e500–e508. doi:10.3399/bjgp14X680989
  4. Etkin, J., 2016. The hidden cost of personal quantification. Journal of consumer research, 42(6), pp.967-984.
  5. Fritz, , E. M.Huang, G. C. Murphy, and T. Zimmermann. 2014. “Persuasive Technology in the Real World: A Study of Long-Term Use of Activity Sensing Devices for Fitness.” Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
  6. Jarrahi,  H., N.Gafinowitz, and G. Shin. 2018. “Activity Trackers, Prior Motivation, and Perceived Informational and Motivational Affordances.” Personal and Ubiquitous Computing 22 (2): 433–448. doi:10.1007/s00779-017-1099-9.
  7. Jin, D., Halvari, H., Maehle, N. and Olafsen, A.H., 2020. Self-tracking behavior in physical activity: a systematic review of drivers and outcomes of fitness tracking. Behaviour & Information Technology, pp.1-20.
  8. Khan, N., Marvel, F.A., Wang, J. and Martin, S.S., 2017. Digital health technologies to promote lifestyle change and adherence. Current treatment options in cardiovascular medicine, 19(8), pp.1-12.
  9. Tung, Y.T., Hsu, Y.J., Liao, C.C., Ho, S.T., Huang, C.C. and Huang, W.C., 2019. Physiological and biochemical effects of intrinsically high and low exercise capacities through multi-omics approaches. Frontiers in physiology, 10, p.1201.
  10. Preusse,  C., T. L.Mitzner, C. B. Fausset, and W. A. Rogers. 2014. “Older Adults’ Changes in Intent to Adopt Wellness Management Technologies.” Paper presented at the Proceedings of the Human Factors and Ergonomics Society Annual Meeting.
  11. Russell, A. P., Lamon, S., Boon, H., Wada, S., Güller, I., Brown, E. L., 2013. Regulation of miRNAs in human skeletal muscle following acute endurance exercise and short-term endurance training. J. Physiol. 591, 4637–4653. doi: 10.1113/jphysiol.2013.255695
  12. Zhu, , S. L.Dailey, D. Kreitzberg, and J. Bernhardt. 2017. “‘Social Networkout’: Connecting Social Features of Wearable Fitness Trackers with Physical Exercise.” Journal of Health Communication 22 (12): 974–980. doi:10.1080/10810730.2017.1382617.
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